A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

By Luis Hernández, Carlos Baladrón Zorita, Javier M. Aguiar, Belén Carro, Antonio J. Sánchez-Esguevillas, Jaime Lloret, Joaquim Massana. IEEE Communications Surveys and Tutorials, 2014

476 citations recorded by the source corpus.

Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the `70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9eef40fe3d812ec0ad9741dcc8f95a292fe97057

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Citations in the corpus

Deep neural networks for energy load forecasting (2017, methodology): “Readers are referred to [17], [18] and [4] for comprehensive surveys of different techniques used for load forecasting.”
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting (2021, methodology): “Based on the application type, the LF is divided into two categories: 1) the time horizon 2) scope of the variables employed [3].”
Building energy load forecasting using Deep Neural Networks (2016, methodology): “For surveys of different techniques used for load forecasting, readers are referred to [16], [17] and [8].”
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols (2016, background): “Another group of surveys has focused on SG pricing methods [91], energy efficiency [92], and power demand forecasting [93], [94], as well as the control of renewable energy in the SG [95].”
A Survey on Smart Grid Cyber-Physical System Testbeds (2017, background): “Utilities and independent system operators seek proper ways to implement future smart grid concepts easily and securely for different application layers, such as metering, monitoring, operation, protection, automation and markets [6], [7].”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “Noticeably, ANN was widely used for various energy-forecasting tasks because of its ability to model nonlinearity [4].”
Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions (2017, background): “Other areas which have been surveyed in the domain of SG include energy efficiency [58], power demand forecasting [59], load balancing [60], optimization and pricing methods [61], and renewable energy [62].”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, background): “STLF is usually carried out over time periods ranging from hours to days or weeks ahead [11].”